Association of Social Determinants of Health and Vaccinations With Child Mental Health During the COVID-19 Pandemic in the US

This cohort study investigates the association of individual and structural social determinants of health and vaccinations with child mental health during the COVID-19 pandemic in the US.

The Adolescent Brain Cognitive Development Study℠ (ABCD Study®), the largest longitudinal study of brain development and child health in the United States, follows over 10 years 11,878 children recruited from 21 U.S. research sites, recruited at ages 9-10 in 2016-18. In March 2020, when our participants were ages 11-to 13-yearsold, the world became affected by the COVID-19 pandemic, leading to an upheaval in the economy and the lives of almost every family. The ABCD Study developed brief surveys sent electronically to all ABCD participants and their participating parent/guardian about the impact of the pandemic on their lives. An overview of the ABCD Study is at https://abcdstudy.org.

eMethods 2. Additional Data Sources
To incorporate individual-and structural-level social determinants of health (SDoH), we assembled a longitudinal dataset, comprising six main components (details in Figure 2, eMethods 1-2): (1) Adolescent Brain Cognitive Development (ABCD) COVID Rapid Response Research (RRR) on child mental health and experiences reported by children and parents/caregivers through six-wave surveys between May 16, 2020 and April 24, 2021; 1,2 (2) ABCD COVID-19 geocoded data on individual-level time-varying SDoH; 2 (3) ABCD child residential history data on structural-level pre-existing SDoH; 3 (4) ABCD baseline children's sociodemographic characteristics (ABCD 4.0 release); 3 (5) Dates where all adults aged 18 years or older were eligible for vaccines, cross-referenced from The New York Times 4 and US News; 5 (6) Centers for Disease Control and Prevention (CDC) COVID-19 Vaccine Tracker. 6 We linked the following databases step by step: 1. ABCD first release -child report 2. ABCD first release -parent report 3. ABCD second release -child report 4. ABCD second release -child report 5. Geocoded metrics data were geocoded concerning the date that the survey was disseminated to the participants, not the date they returned their surveys. b. COVID-19 prevalence (i.e., case and death counts) were obtained from a publicly available GitHub repository maintained by the Center of Systems Science and Engineering at Johns Hopkins University. Data sources are cited on their README.md found at https://github.com/CSSEGISandData/COVID-19. These data have the county-level resolution. Raw case and death counts (cumulative) were used to calculate new case/death counts and aggregated rolling 7-day averages. Metrics were population adjusted by U.S. Census measures per county: https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html c. Geocoded metrics data were geocoded concerning the date that the survey was disseminated to the participants, not the date they returned their surveys. Daily metrics for mobile device behaviors are publicly available by SafeGraph at the census block level from 2019 through Dec 2020. Based on the SafeGraph website description, "This product is delivered daily (3 days delayed from actual). Daily data is available going back to January 1, 2019. We used v2.1 to create the historical data from January 1, 2019 -to December 31, 2019 (the backfill) and the data from May 10, 2020, forwards. The data was generated using a panel of GPS pings from anonymous mobile devices. We determine the common nighttime location of each mobile device over 6 weeks to a Geohash-7 granularity (~153m x ~153m). We call this common nighttime location the device's "home" for ease of reference. We then aggregate the devices by home census block group and provide the metrics set out below for each census block group." The goal of incorporating the social distancing is to capture the physical mobility of parents/caregivers and children in ABCD. Despite the social distance policies by states, some low-wage essential workers may still need to go to full-time work and not work remotely. "full-time" working behaviors in SafeGraph were used as a proxy for working full-time. We note that this measure may include time spent at school for some individuals. Individuals must be 13 years or older to be included in the SafeGraph sample. Thus, the sample does not include elementary and middle school students, but high school and college students may be included. We acknowledge that we may classify being at school as being at work using our variable definition for this latter group of students. d. Baseline demographic characteristics were reported by the parent/caregiver on the Parent Demographics Survey as described by Barch and colleagues. 7,8 Race and ethnicity were categorized into five categories, as the 'race_ethnicity' variable from the ABCD ACS Post Stratification Weights data (short name: acspsw03) in the ABCD 4.0 Release. The "Other" categories contain answers by parents who chose other racial groups or multiple racial identities. e. Eligibility dates for vaccination among adults. Data were cross-referenced from the New York Times 4 and US News, 5 where exact dates were collected from state and county health departments. However, the pace of vaccinations varies across the country. Geographic variation in vaccinations may be influenced by priority groups, vaccine eligibility, types and strengths of vaccine mandate, mistrust in vaccinations, logistical and bureaucratic challenges in distributions and administrations, and social vulnerability. In our main analysis, we chose the dates when all adults were eligible for vaccination as a proxy for the state's vaccine priority and implementation. We further use the C.D.C. state-level vaccination rates reported on April 30, 2021 (the same month as the last interview reported in the ABCD RRR second data release). We divided the original rates by 4 quantiles for comparisons, with higher quantiles representing a greater percentage of adults aged 18 and above fully vaccinated (had the second dose of a vaccine), which is the variable Series_Complete_18PlusPop_Pct in the C.D.C. raw data. We substituted it with percent of people who are fully vaccinated (have a second dose of a two-dose vaccine or one dose of a single-dose vaccine) based on the jurisdiction where the recipient lives (Series_Complete_Pop_Pct), percent of the population with at least one dose based on the jurisdiction where recipient lives (Administered_Dose1_Pop_Pct), Percent of 18+ population with at least one dose based on the jurisdiction where the recipient lives (Administered_Dose1_Recip_18PlusPop_Pct4), and percent of delivered doses per 100,000 census population (Dist_Per_100K). Results were consistent with the main study results. The distributions of states by adult vaccine eligibility and quantiles of fully vaccinated adults rates are below:

Dates when Adults were Eligible for Vaccine Percent of adults aged 18 and above fully vaccinated
Before  Random effects include random intercepts for subject ID and study site. Random effects were restricted to be uncorrelated. We used interview dates, parameterized as the dates since the first day of data collection (May 16, 2020), as the time trend indicator in the random slope (change per survey date). We did not use the survey numbers as the time variable, because as indicated in the first ABCD COVID Rapid Response Release, Surveys 1-3 did not have a link expiration date when distributed. Thus, parent or youth participants might have completed two surveys at once, and/or the dates for the parent and youth surveys could differ. We identified and excluded these out-of-order observations as recommended. We also check the interview_date variable to assess these issues.
Across the four mental health outcomes, we separately estimated the linear and nonlinear (quadratic, cubic) trajectories over time. We determined the final model by assessing the Akaike information criteria [AIC] and Bayesian information criteria [BIC]) and comparing the likelihood ratio between nested models. 9,10 Model fit indices suggest nonlinear (quadratic) trajectories of PSS and NIH-TB Sadness, and linear trajectories of COVID-19 worry and NIH-TB positive affect.
Results of multilevel generalized mixed effect modeling were presented using estimated intercepts (baseline scores) and regression coefficients (β) with 95% CIs.

Restricted cubic splines
We modeled PSS trajectories using restricted cubic splines with 95% CIs relative to the median. Knots were chosen using the Harrell method. 11 Cubic splines are preferred over traditional techniques (e.g., linear models, mandatory categorization) 12  We included a series of interaction terms between SDoH characteristics and the splines for days since May 16, 2020, to determine whether the changes in mental health during COVID-19, differed across SDoH factors. 13 We use post hoc Wald tests to determine whether the differences in child mental health trajectories across SDoH factors over time were significant. We used the margins and xbrcspline to generate linear predictions of mental health and to generate the differences in the level of mental health indicators, on given survey dates compared to May 16, 2020, respectively. 14 eMethods 5. Consideration of Clustering and Population-Based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Data ABCD is a longitudinal, observational study of U.S. children, ages 9-10 at baseline, recruited at random from the household populations in defined catchment areas for each of 21 study sites. The 21 geographic locations that comprise the ABCD research sites are nationally distributed and generally represent the range of demographic and socioeconomic diversity of the U.S. birth cohorts that comprise the ABCD study population. The clustering of participants and the potential for selection bias in study site selection and enrollment are features of the ABCD observational study design that are informative for statistical estimation and inference. 15 In this study, we referred to recommended robust and efficient approaches to consider two important nested nature of ABCD based on our research questions. 1. Clustering and non-independence of observations Primary analyses employed generalized linear mixed-effects models (GLMM). To adjust for the nested nature, the ABCD study site and participant ID were included as random effects. We did not include family-level random intercepts as our main associations of interest in this study are the long-term individual trajectories and site-level SDoH variables. Besides, trying to include family-random intercepts in this complex model led to overfitting and singularity problems. In a sensitivity analysis, we compare the model fit and results using three-level GLMM, comparable to the current findings.

American Community Survey (ACS) propensity weights
We did not use the ACS propensity weights as the purpose of our study is not to obtain estimates of the examined associations that could be generalized as population-representative (i.e., modeling site characteristics, assuming pseudo-randomization of age, sex, race/ethnicity, family type, parental employment status, family size and Census region). 16 Instead, our study aims to take advantage of the multisite nature of the study to examine factors that might contribute to site-specific SDoH effects across the 21 geographic locations and 17 States.
In addition, as indicated in the first and second Release Notes of the Adolescent Brain Cognitive Development Study℠ (ABCD) COVID Rapid Response Research (RRR) Survey data, all Surveys 1-6 were collected through electronic surveys sent to all ABCD participants and their participating parent/guardian. There may also be selection bias of online survey design that cannot be accounted for by the ACS propensity weights.
Therefore, based on our understanding of it, we think using the ACS propensity weighting would reduce the interpretability of our findings.
In a sensitivity analysis, we compare the model fit and results using multilevel GLMM and the design-based weighted regression, results were comparable to the current findings. eMethods 6. Sensitivity Analysis We conducted several sensitivity analyses.
First, we investigated potential nonlinear trajectories by adding quadratic survey dates after the first dissemination to the mixed-effect models. The final model was selected based on AIC and BIC. Nonlinear models with the quadratic terms of time fit better for perceived stress and NIH-TB sadness, whereas linear models better predicted COVID-19 worry and NIH-TB positive affect in our sample.
Second, to investigate the racial and ethnic differences in the associations between SDoH and child mental health, we tested the interaction effects between race and ethnicity and each SDoH indicator. Results were reported in eFigures 5-24.
Third, we divided the study sample randomly, repeated the GLMM in a training sub-sample and validation subsample. Results using the training sample can be replicated in the second half of the data.
Fourth, following the NIH Toolbox® Emotion Batteries Scoring and Interpretation Guide for the iPad 17 29 we dichotomized the NIH-TB Sadness and Positive Affect T-scores into whether the children warrant heightened surveillance or concern (>=60) or not (<60), and examined models for trajectories of sadness and positive affect assuming a binomial distribution and a logit link function. Again, the main findings were unchanged.
Fifth, for vaccination data, we referred to the CDC state-level vaccination rates reported on April 30, 2021 (the same month as the last interview date reported in the ABCD RRR second data release), matching with the states where the ABCD children were recruited. We used the percent of people 18+ who are fully vaccinated (have second dose of a two-dose vaccine or one dose of a single-dose vaccine) based on the jurisdiction where the recipient lives (variable name: Series_Complete_18PlusPop_Pct) for the main sensitivity analyses. We also replicated the same set of analyses (explained below) with Lastly, we conducted all analyses among saturated samples without missing SDoH factors and mental health outcomes. Results of the sensitivity analyses suggested that nonresponse bias did not threaten the validity of our findings. Statistical significance was determined by a 2-sided P value <.05. Analyses and visualization were conducted using Stata (version 17), R, and Python 3.9.  Participant data were excluded if they reported an invalid primary residential address (n=1791), if the ADI score was missing or invalid (n=403), and if there were missing data for age, sex, race, ethnicity, household income, parental education, and parental marital status (n = 3801). As recommended by the ABCD Release notes, 1 we excluded observations where a participant returned the questionnaire after the dissemination of the subsequent survey (e.g., question in Survey 1 was completed after Survey 2 was completed, n=369). For households with siblings, we randomly selected one child in each family to adjust the issues of convergence of random effects. Combining ABCD COVID-19 surveys, main study data, and geocoded information resulted in 52 197 observations across 9799 participants. We included participants with at least three repeated measures for robust estimation of child mental health trajectories and excluded invalid data. The study sample contained 44 958 observations. We kept all records with available information. Note. Race and ethnicity were based on parent-reported demographic characteristics in the ABCD baseline sample (shortname = acspsw03). We used Census-tract-level Area Deprivation Index (ADI) for children's primary residential address at the baseline visit. ADI is a composite weighted metric of 17 neighborhood disadvantage indicators (e.g., poverty rates, unemployment, median family income, low education; see eTable S1). Geocoded ADI in ABCD was based on the 2011-2015 five-year ACS estimates and discretized into national percentiles. We selected ADI as it does not include indirect measures of race and ethnicity in its construction and can limit possible confounding. Based on the percentiles, we categorized ADI into three levels: most, mild, and least deprived areas. The racial and ethnic differences in ADI distribution indicated the need to include both factors in the model.